Research on the Application and Optimization of Convolutional Neural Networks in Digital Audit Platforms

Security is an eternal topic, especially in the era of mobile internet, where various security vulnerabilities emerge endlessly, making the already busy security work even more like walking on thin ice. With the gradual popularization of internet applications, people transmit more important information through the internet, but the losses caused by information leakage to people's work and life will also increase. Security auditing, as a key focus of security management work, has a good deterrent effect on individuals with abnormal behavior. A user behavior prediction model based on Convolutional Neural Network (CNN) is proposed to address the issue of individual behavior in a group having randomness and uncertainty but tending to adopt the same or similar strategies in the group structure. This method first conducts network operation analysis to extract user operations from the original operation record information; Then explore the correlation between network operations and user behavior, and construct a similarity matrix between network operations and user behavior; Finally, extract user behavior. The simulation results show that the model can effectively improve the accuracy of user behavior prediction, and effectively curb the occurrence of security incidents and risks by implementing quasi real-time auditing of user behavior.